plot.ensemble {randomSurvivalForest}R Documentation

Plot of Ensemble Estimates

Description

Plot ensemble survival curves and ensemble estimates of mortality.

Usage

    plot.ensemble(x, plots.one.page = TRUE, ...)

Arguments

x An object of class (rsf, grow) or (rsf, predict).
plots.one.page Logical. Should plots be placed on one page? Default is TRUE.
... Further arguments passed to or from other methods.

Details

Four plots are produced. Going from top to bottom, left to right: (1) Ensemble survival function for each individual. Thick red line is overall ensemble survival, thick green line is Nelson-Aalen estimator. (2) Comparison of the population ensemble survival function to the Nelson-Aalen estimator. (3) Brier score (0=perfect, 1=poor, and 0.25=guessing) stratified by ensemble mortality. Based on the method described in Gerds et al. (2008) in which the censoring distribution is estimated using the Kaplan-Meier estimator. Stratification is into 4 groups corresponding to the 0-25, 25-50, 50-75 and 75-100 percentile values of mortality. Red line is the overall (non-stratified) Brier score. (4) Plot of mortality versus observed time. Points in blue correspond to events, black points are censored observations.

Note that when x is of class (rsf, predict) not all plots will be produced.

For competing risk analyses plots correspond to unconditional values (i.e., they are non-event specific). Use competing.risk for event-specific curves and for a more comprehensive analysis in such cases.

Whenever possible, out-of-bag (OOB) values are used.

Author(s)

Hemant Ishwaran hemant.ishwaran@gmail.com and Udaya B. Kogalur ubk2101@columbia.edu

References

T.A. Gerds, T. Cai and M. Schumacher (2008). The performance of risk prediction models, Biometrical Jour., 4:457-479.

E. Graf, C. Schmoor, W. Sauerbrei and M. Schumacher M (1999). Assessment and comparison of prognostic classification schemes for survival data, Statistics in Medicine, 18:2529-2545.

H. Ishwaran, U.B. Kogalur (2007). Random survival forests for R, Rnews, 7/2:25-31.

See Also

competing.risk, rsf, predict.rsf.

Examples

data(veteran, package = "randomSurvivalForest") 
v.out <- rsf(Survrsf(time, status)~., veteran, ntree = 1000)
plot.ensemble(v.out)

# plot of ensemble survival for a single individual
surv.ensb <- t(exp(-v.out$oob.ensemble))
plot(v.out$timeInterest, surv.ensb[, 1])


[Package randomSurvivalForest version 3.6.1 Index]